Let's start to understand the structure of game AI systems by taking a
virtual microscope and looking inside a single AI entity. It can be a
Quake enemy, an Age of Empires army, or the creature from Black
& White. Understanding the major building blocks will later help you
structure and code your systems efficiently.

Fundamentally, AI systems come in two flavors. The first and most common is
the agent, which is a virtual character in the game world. These are usually
enemies, but can also be nonplaying characters, sidekicks, or even an animated
cow in a field. For these kinds of entities, a biological structure must be
followed, so we can somehow model their behavior realistically. Thus, these AI
systems are structured in a way similar to our brain. It is easy to identify
four elements or rules:

A sensor or input system

A working memory

A reasoning/analysis core

An action/output system

Some AIs are simpler than that and override some components. But this global
framework covers most of the entities that exist. By changing the nature of each
component, different approaches can be implemented.

The second type of AI entity is abstract controllers. Take a strategy game,
for example. Who provides the tactical reasoning? Each unit might very well be
modeled using the preceding rules, but clearly, a strategy game needs an
additional entity that acts like the master controller of the CPU side of the
battle. This is not an embodied character but a collection of routines that
provide the necessary group dynamics to the overall system. Abstract controllers
have a structure quite similar to the one explained earlier, but each subsystem
works on a higher level than an individual.

Let's briefly discuss each element of the structure.

Sensing the World

All AIs need to be aware of their surroundings so they can use that
information in the reasoning/analysis phase. What is sensed and how largely
depends on the type of game you are creating. To understand this, let's
compare the individual-level AI for a game like Quake to the abstract
controller from Age of Empires.

In Quake, an individual enemy needs to know:

Where is the player and where is he looking?

What is the geometry of the surroundings?

Sometimes, which weapons am I using and which is he using?

So the model of the world is relatively straightforward. In such a game, the
visual system is a gross simplification of the human one. We assume we are
seeing the player if he's within a certain range, and we use simple
algorithms to test for collisions with the game world. The sensory phase is
essential to gathering information that will drive all subsequent analysis.

Now let's take a look at the sensory data used by the master controller
in a strategy game, such as Age of Empires:

What is the balance of power in each subarea of the map?

How much of each type of resource do I have?

What is the breakdown of unit types: infantry, cavalry, and so
on?

What is my status in terms of the technology tree?

What is the geometry of the game world?

Notice that these are not simple tests. For example, we need to know the
geometry of the whole game world to ensure that the path finding works as
expected for all units. In fact, the vast majority of the AI time in such a game
is spent in resolving path-finding computations. The rest of the tests are not
much easier. Computing the balance of power so we know where the enemy is and
his spatial distribution is a complex problem. It is so complex that we will
only recompute the solution once every N frames to maintain decent
performance.

In many scenarios, sensing the game world is the slowest part of the AI.
Analyzing maps and extracting valuable information from raw data is a
time-consuming process.